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Article

Learning class-discriminative dynamic Bayesian networks

Authors:

Terran LaneAuthors Info & Claims

ICML '05: Proceedings of the 22nd international conference on Machine learning

Pages 97 - 104

https://doi.org/10.1145/1102351.1102364

Published: 07 August 2005 Publication History

Abstract

In many domains, a Bayesian network's topological structure is not known a priori and must be inferred from data. This requires a scoring function to measure how well a proposed network topology describes a set of data. Many commonly used scores such as BD, BDE, BDEU, etc., are not well suited for class discrimination. Instead, scores such as the class-conditional likelihood (CCL) should be employed. Unfortunately, CCL does not decompose and its application to large domains is not feasible. We introduce a decomposable score, approximate conditional likelihood (ACL) that is capable of identifying class discriminative structures. We show that dynamic Bayesian networks (DBNs) trained with ACL have classification efficacies competitive to those trained with CCL on a set of simulated data experiments. We also show that ACL-trained DBNs outperform BDE-trained DBNs, Gaussian naïve Bayes networks and support vector machines within a neuroscience domain too large for CCL.

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Cited By

Bielza CLarrañaga P(2014)Discrete Bayesian Network ClassifiersACM Computing Surveys10.1145/257686847:1(1-43)Online publication date: 14-Jul-2014
https://dl.acm.org/doi/10.1145/2576868
Drugan MWiering M(2010)Feature selection for Bayesian network classifiers using the MDL-FS scoreInternational Journal of Approximate Reasoning10.1016/j.ijar.2010.02.00151:6(695-717)Online publication date: 1-Jul-2010
https://dl.acm.org/doi/10.1016/j.ijar.2010.02.001
Li NYu YZhou Z(2009)Semi-naive Exploitation of One-Dependence EstimatorsProceedings of the 2009 Ninth IEEE International Conference on Data Mining10.1109/ICDM.2009.64(278-287)Online publication date: 6-Dec-2009
https://dl.acm.org/doi/10.1109/ICDM.2009.64
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Published In

cover image ACM Other conferences

ICML '05: Proceedings of the 22nd international conference on Machine learning

August 2005

1113 pages

ISBN:1595931805

DOI:10.1145/1102351

General Chair:
Saso Dzeroski
Jozef Stefan Institute, Slovenia
,
Program Chairs:
Luc De Raedt,
Stefan Wrobel

Copyright © 2005 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 August 2005

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Cited By

Bielza CLarrañaga P(2014)Discrete Bayesian Network ClassifiersACM Computing Surveys10.1145/257686847:1(1-43)Online publication date: 14-Jul-2014
https://dl.acm.org/doi/10.1145/2576868
Drugan MWiering M(2010)Feature selection for Bayesian network classifiers using the MDL-FS scoreInternational Journal of Approximate Reasoning10.1016/j.ijar.2010.02.00151:6(695-717)Online publication date: 1-Jul-2010
https://dl.acm.org/doi/10.1016/j.ijar.2010.02.001
Li NYu YZhou Z(2009)Semi-naive Exploitation of One-Dependence EstimatorsProceedings of the 2009 Ninth IEEE International Conference on Data Mining10.1109/ICDM.2009.64(278-287)Online publication date: 6-Dec-2009
https://dl.acm.org/doi/10.1109/ICDM.2009.64
Kim DBurge JLane TPearlson GKiehl KCalhoun V(2008)Hybrid ICA–Bayesian network approach reveals distinct effective connectivity differences in schizophreniaNeuroImage10.1016/j.neuroimage.2008.05.06542:4(1560-1568)Online publication date: Oct-2008
https://doi.org/10.1016/j.neuroimage.2008.05.065
Burge JLane T(2007)Shrinkage Estimator for Bayesian Network ParametersProceedings of the 18th European conference on Machine Learning10.1007/978-3-540-74958-5_10(67-78)Online publication date: 17-Sep-2007
https://dl.acm.org/doi/10.1007/978-3-540-74958-5_10
Burge JLane TLink HQiu SClark V(2007)Discrete dynamic Bayesian network analysis of fMRI dataHuman Brain Mapping10.1002/hbm.2049030:1(122-137)Online publication date: 7-Nov-2007
https://doi.org/10.1002/hbm.20490
Lee YKang JKang BRyu K(2006)Bayesian Sampling of Virtual Examples to Improve Classification Accuracy2006 SICE-ICASE International Joint Conference10.1109/SICE.2006.315740(1009-1014)Online publication date: Oct-2006
https://doi.org/10.1109/SICE.2006.315740
Lee YKang JKang BRyu K(2006)Sampling of virtual examples to improve classification accuracy for nominal attribute dataProceedings of the 5th international conference on Rough Sets and Current Trends in Computing10.1007/11908029_66(637-646)Online publication date: 6-Nov-2006
https://dl.acm.org/doi/10.1007/11908029_66
Burge JLane T(2006)Improving bayesian network structure search with random variable aggregation hierarchiesProceedings of the 17th European conference on Machine Learning10.1007/11871842_11(66-77)Online publication date: 18-Sep-2006
https://dl.acm.org/doi/10.1007/11871842_11

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